Contributed Commentary by Matthew Thompson, Associate Director, Statistical Programming, Phastar 

March 14, 2025 | Interest in Real-World Evidence (RWE) studies continues to grow. Data from ClinicalTrials.gov shows the number of RWE studies registered has increased dramatically since 2005, particularly in the last seven years. But what is RWE, how is it used in clinical trials and how can we work with Real-World Data (RWD) to produce RWE? 

RWE versus RWD 

The European Medicines Agency (EMA) defines Real-World Data (RWD) as data describing patient characteristics, including treatment utilization and outcomes, in routine clinical practice and RWE as evidence derived from the analysis of RWD. The U.S. Food and Drug Administration (FDA) defines RWD as data relating to patient health status and/or the delivery of health care which is routinely collected from a variety of sources. It defines RWE as clinical evidence regarding a medical product’s use and potential benefits or risks derived from analysis of RWD. 

The 21st Century Cures Act (2016), intended to accelerate medical product development and bring innovation faster and more efficiently to the patients who need it, resulted in the FDA publishing a framework to evaluate the potential use of RWE in regulatory decision-making for drugs and biologics.  

Regulators have also committed to international collaboration to enable RWE for regulatory decision making in four key areas: harmonization of RWD and RWE terminologies, convergence on guidance, readiness and transparency. 

How is RWE used in Clinical Trials? 

Reliance on RWD increases as we move from traditional randomized controlled trials (RCTs) to observational studies. There are many sources of RWD, some of the most commonly used ones include Electronic Health Records (EHRs), Administrative Claims, Personal Digital Health Applications, Patient Registries and Public Health Databases. Each data source has its own characteristics and successful utilization of it brings its own challenges and benefits. It is important to understand the source you are using and its suitability for your study. 

In recent years, recognition of the value of RWE has grown among healthcare stakeholders because of its ability to provide insights into the healthcare delivery and utilization outside controlled clinic trials. RWE is increasingly used to inform decision making for drug development across the total product life cycle, from clinical development through to post market. 

There are several research questions which can be answered using RWD:  

• How can we increase knowledge about specific patient groups or subgroups in terms of population characteristics?
• What is the treatment effect in a subgroup of the patient population and for specific treatment patterns? 
•  What is the safety profile of the treatment over a longer term? 
• What is the natural history of a specific condition? 

There are also many potential RWE study objectives including: 

• Developing and validating digital health technologies including prognostic models.
• Estimating economic burden, resource use, and costs.
• Summarizing interventions, care pathways, and treatment patterns.
• Estimating device or procedure failure rates.
• Estimating test accuracy or reproducibility of test results.
• Describing patient experience. 

How RWE Accelerates Trial Outcomes 

RWE can significantly reduce the time required to achieve meaningful clinical insights, particularly in the following ways: 

• Enabling hybrid trial designs: By integrating RWD into clinical trials, hybrid designs can be developed that allow for faster recruitment, reduced patient burden, and lower costs. For example, historical control arms derived from RWD can be used instead of recruiting a full control cohort in certain studies.
  
• Faster patient recruitment: By leveraging EHRs and claims data, eligible patient populations can be identified more efficiently, allowing for quicker trial initiation and enrolment.
  
• Adaptive trial designs: Continuous data streams from RWD sources facilitate adaptive trials where adjustments can be made in real-time based on interim analyses.
  
• Post-market evidence generation: RWE can provide evidence of safety and effectiveness in real-world populations that would take years to observe in traditional long-term trials. This accelerates regulatory decisions, especially for therapies targeting rare diseases or urgent unmet needs. 

The Role of Specialist Biometrics Providers 

Working with specialist biometrics providers enhances trial outcomes by leveraging advanced analytics, robust data management, and innovative study designs. Key contributions include: 

RWE Study Design 

RWD can be collected as either primary or secondary data. When designing an RWE study, many of the considerations are consistent with those of an RCT. However, there are some key differences which it is important to be aware of. 

Eligibility criteria need to reflect the population of interest. In an RWE study we do not have the barrier of people not wanting to participate because they prefer a particular treatment. However, there are different challenges. For example, treatment environments are rapidly evolving, and retrospective data may not be representative going forward. Careful consideration of the eligibility criteria is needed to avoid bias.  

Using an appropriate treatment effect comparator can also be challenging because RWE studies are often in effect single arm trials which can itself lead to bias. Randomized treatment assignments are not usually an option in an RWE study. However, there are statistical approaches like propensity score methods which can emulate a RCT using observational data.  

Start of follow up in an RWE study is often ill-defined but it is crucial to define the index dates clearly and in a way that avoids bias. For example, a situation where a patient must have survived for a certain amount of time for them to be included in the study can create bias in terms of survival outcomes. The end of follow up can also be problematic because there can be more intercurrent events compared with an RCT, such as treatment switching or a dropout lack of retention.